Recently, I took part in an AiM Sports data analysis workshop at Winding Road Racing in Austin: these workshops are offered nationwide throughout the year, conducted by factory experts and racers. I highly recommend looking for and attending one.
The workshop helped me make sense of the huge amount of data that my AiM MXL captures. Racing Shifter karts I relied on this data, and in Formula Mazda it’s no different: data is absolutely critical. Because with data, I can make better changes to my strategies, faster. In this workshop I learned to understand both the software and tools, thus I can spend less time looking at data and more time developing as a driver. And now that I know how to read the data, I want to find a way to visually compare lap data between drivers, to help drivers develop. This includes me!
Looking at my own data is cool, but comparing it with other drivers is even cooler. I needed a way to accurately compare lap times between multiple drivers and different manufacturers. This is where I had to get creative: my competitors may be competing in different cars! But I can still learn from them.
I race a Formula Mazda in SCCA Formula X, a national conglomerate class of F2000, Formula 4, Formula Mazda and Pro Mazda, and other open-wheel cars. So instead of looking purely at, say, lap times or race lines, both of which are heavily dependent of the car class, I chose to look at lap consistency and the way in which lap times progressed during racing sessions. To do this, I selected a set of drivers that are in my class, other drivers that are in different classes, and drivers of varying abilities. Then, using statistical analysis applied to lap times I was able to calculate a standard deviation for each driver in a given session. Looking at my own and my competitors’ 95th percentiles, I was able to visualize and standardize a measure of consistency around the average lap time in each session and determine how consistent the driver was lap-to-lap.
Bottom line: Every driver has a unique style, and each car class has a distinct profile. By evaluating consistency rather than lap or segment time it’s easy to spot the opportunities for development using standard deviation and lap average.
The first step was creating a chart with standard deviation across the five drivers and using that data to create lap averages for the drivers. You can the lap times pick up as a group after the start lap, and as the race progressed. And of course, you see the obvious difference due to both car class and driver ability.
I then created a chart rule to show anything slower than average in cascading shades of red, and any time faster in shades of green. That made it easy to spot visualize consistency: bright red/ bright green are outliers, whereas the lighter shades around the average are laps that are more consistent.
I took my final data and created two charts. The top chart shows each driver's lap time across the race, while the bottom is showing standard deviation of the selected driver. It’s this chart that I think is the most useful for driver development: a series of slightly slower laps that are all within a few hundredths of each other is a sign of consistency, whereas a couple of flyers and a bunch of off-laps is a sign of inconsistency.
For me, organizing lap data in this kind of layout helps me visually determine my consistency and driver progress as my season and my own development progress. My experience is that with focus, a consistent driver can improve more quickly than a faster driver who is only faster “sometimes.” And of course, lap consistency is only one piece of data: combining this with rpm, g-forces, and more advanced driver input monitors (e.g., pedal position) offers a more complete view. In my next post I’ll look at “friction circles,” another essential element in understanding the connection between the driver and car by looking quantitatively at the connection between the car and track surface.